"It is commonly believed that individuals must provide a copy of their personal information in order for AI to train or predict over it. This belief creates a tension between developers and consumers. Developers want the ability to create innovative products and services, while consumers want to avoid sending developers a copy of their data.

With OpenMined, AI can be trained on data that it never has access to.

The mission of the OpenMined community is to make privacy-preserving deep learning technology accessible to consumers, who supply data, and machine learning practitioners, who train models on that data. Given recent developments in cryptography, AI-based products and services do not need a copy of a dataset in order to create value from it."

HACKATHONOn Saturday, January 13th, the OpenMined community will be gathering in-person in over 20 cities around the world to collaborate on various coding projects and challenges. We’ll have a worldwide video hangout for all who cannot make it to a physical location. The hackathon will include three coding projects, each with a live tutorial from a member of the OpenMined community.

Here are the general details:

OpenMined Hackathon DetailsDate: January 13, 2018On Saturday, January 13th, the OpenMined community will be gathering in-person in over 20 cities around the world to collaborate on various coding projects and challenges. We’ll have a world-wide video hangout for all who cannot make it to a physical location. The hackathon will include three coding projects, each with a live tutorial from a member of the OpenMined community. While hackathons will start at the discretion of each city’s organizer (slack them for details), code tutorials will be live broadcasted at 3 different times: 12:00 noon London time, 12:00 noon Eastern time, and 12:00 noon Pacific Time.Coding ProjectsBeginner: Build a Neural Network in OpenMinedPresentation: How to use the OpenMined Keras InterfaceProject: Find a new dataset and train a new neural network using the Keras interface!Intermediate: Building the Guts of a Deep Learning FrameworkPresentation: How OpenMined Tensors Work - The Magic Under the HoodProject: Add a feature to Float TensorsAdvanced: Performance Improvements - GPUs and NetworkingPresentation: Optimizing the key bottlenecks of the systemProject: The Need for Speed - Picking a Neural Network and Making it FasterPhysical LocationsParticipants in this hackathon will meet in person at the following locations. If your city says “venue tbd”, reach out to the Slack Point of Contact for specific details and directions. Starbucks is the suggested backup venue of choice - usually has fast wifi and big tables available. (If you aren’t on our Slack, click here for an inviteBefore you come...you need to do the following